5,543 research outputs found
Neural activity classification with machine learning models trained on interspike interval series data
The flow of information through the brain is reflected by the activity
patterns of neural cells. Indeed, these firing patterns are widely used as
input data to predictive models that relate stimuli and animal behavior to the
activity of a population of neurons. However, relatively little attention was
paid to single neuron spike trains as predictors of cell or network properties
in the brain. In this work, we introduce an approach to neuronal spike train
data mining which enables effective classification and clustering of neuron
types and network activity states based on single-cell spiking patterns. This
approach is centered around applying state-of-the-art time series
classification/clustering methods to sequences of interspike intervals recorded
from single neurons. We demonstrate good performance of these methods in tasks
involving classification of neuron type (e.g. excitatory vs. inhibitory cells)
and/or neural circuit activity state (e.g. awake vs. REM sleep vs. nonREM sleep
states) on an open-access cortical spiking activity dataset
Modifying the Symbolic Aggregate Approximation Method to Capture Segment Trend Information
The Symbolic Aggregate approXimation (SAX) is a very popular symbolic
dimensionality reduction technique of time series data, as it has several
advantages over other dimensionality reduction techniques. One of its major
advantages is its efficiency, as it uses precomputed distances. The other main
advantage is that in SAX the distance measure defined on the reduced space
lower bounds the distance measure defined on the original space. This enables
SAX to return exact results in query-by-content tasks. Yet SAX has an inherent
drawback, which is its inability to capture segment trend information. Several
researchers have attempted to enhance SAX by proposing modifications to include
trend information. However, this comes at the expense of giving up on one or
more of the advantages of SAX. In this paper we investigate three modifications
of SAX to add trend capturing ability to it. These modifications retain the
same features of SAX in terms of simplicity, efficiency, as well as the exact
results it returns. They are simple procedures based on a different
segmentation of the time series than that used in classic-SAX. We test the
performance of these three modifications on 45 time series datasets of
different sizes, dimensions, and nature, on a classification task and we
compare it to that of classic-SAX. The results we obtained show that one of
these modifications manages to outperform classic-SAX and that another one
slightly gives better results than classic-SAX.Comment: International Conference on Modeling Decisions for Artificial
Intelligence - MDAI 2020: Modeling Decisions for Artificial Intelligence pp
230-23
Predictive Maintenance on the Machining Process and Machine Tool
This paper presents the process required to implement a data driven Predictive Maintenance (PdM) not only in the machine decision making, but also in data acquisition and processing. A short review of the different approaches and techniques in maintenance is given. The main contribution of this paper is a solution for the predictive maintenance problem in a real machining process. Several steps are needed to reach the solution, which are carefully explained. The obtained results show that the Preventive Maintenance (PM), which was carried out in a real machining process, could be changed into a PdM approach. A decision making application was developed to provide a visual analysis of the Remaining Useful Life (RUL) of the machining tool. This work is a proof of concept of the methodology presented in one process, but replicable for most of the process for serial productions of pieces
Eddy current defect response analysis using sum of Gaussian methods
This dissertation is a study of methods to automatedly detect and produce approximations of eddy current differential coil defect signatures in terms of a summed collection of Gaussian functions (SoG). Datasets consisting of varying material, defect size, inspection frequency, and coil diameter were investigated. Dimensionally reduced representations of the defect responses were obtained utilizing common existing reduction methods and novel enhancements to them utilizing SoG Representations. Efficacy of the SoG enhanced representations were studied utilizing common Machine Learning (ML) interpretable classifier designs with the SoG representations indicating significant improvement of common analysis metrics
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